A Novel Dual-Channel Hybrid Attention Model for Wind Turbine Misalignment Fault Diagnosis

Aiming at the problems of inaccurate feature extraction, slow convergence, and low diagnostic accuracy of wind turbine misalignment fault diagnosis under complex working conditions, this paper proposes an innovative diagnostic method based on two channels of U-Net and ResNet50. The model innovatively introduces the multi-head attention mechanism (MHA) in the jump connection of the U-Net architecture to form hybrid U-Net and optimizes the feature fusion process with dynamically learnable weights, which significantly enhances the ability to capture local details and key fault features. In the ResNet50 branch, deep global features are fully mined for extraction. To further achieve the co-optimization of global and local information, a shared hybrid expert attention (SHEA) module is proposed. This module achieves efficient integration of features by adaptively fusing the multi-scale local features output from the hybrid U-Net decoder with the deep global features extracted from the ResNet50 backbone network through a dynamic weighting and expert selection mechanism. The multi-scale features optimized by the SHEA module are fed into the classifier for fault type determination. The experimental results show that the method demonstrates excellent convergence speed and 99.64% classification accuracy under complex working conditions, providing an effective solution for the intelligent diagnosis of wind turbine misalignment faults.